Reality check: The true pace and payoffs of AI adoption in corporate real estate

• Corporate Real Estate (CRE) is at the dawn of an AI transformation. The number of companies running CRE AI pilots has exploded from 5% to 92% in just three years, but we are still in the early experimentation phase, with most organizations learning what works before scaling to full implementation.
• Strategic priorities drive AI pilot selection. Facing budget pressure, CRE teams are focusing their resources on high-impact areas like portfolio optimization, energy management and data-related workflows that align with C-suite business priorities, rather than pursuing ‘low-hanging fruit’ applications.
• Companies lagging in technology adoption face a widening gap in AI success. Despite unprecedented enthusiasm, many companies lack a systematic approach to AI implementation, widening the competitive gap between organizations who already have a successful tech program and those who are lagging.
   

Throughout 2025, Corporate Real Estate (CRE) professionals are awash with AI speculations—from bold predictions about transformation to skeptical warnings about overhype. Amid all the noise, decision-makers need to separate fact from fiction and have clarity on the true pace and payoffs of AI adoption in CRE. 

Based on insights from JLL's 2025 Global Real Estate Technology Survey (1,000+ senior CRE decision-makers in major industries across 16 markets), this analysis cuts through the hype to reveal where meaningful value is emerging, what separates successful AI implementations from expensive experiments and how the companies getting it right today are preparing themselves for waves of change we can't yet fully predict.

This isn't just a story about technology maturity – it's about strategic choices, organizational capabilities and systematic approaches that separate the 5% achieving real results from the 95% still searching for their breakthrough.

Over the past decade, key CRE technologies have gradually reached maturity. Technologies such as environmental sensors, data modeling tools and predictive maintenance solutions are now adopted by over 80% of large occupiers, driving concrete value in physical workplace and employee experience.

Now AI, once a subset of the technologies explored by only a handful of CRE teams, dominates nearly all real estate tech innovation discussions. The speed of this pivot has been unprecedented. Just two years ago, under 5% of occupiers had plans to embed AI in CRE operations. Today, 92% of CRE teams have started piloting AI, or plan to start this year — a speed that outstrips even optimistic industry predictions.

This urgency is reshaping technology budgets. Real estate tech spending has been reorganized around AI initiatives, with the top 5 budget priorities all relating to implementing AI or preparing for its impact through upgraded cybersecurity and digital infrastructure.

However, this budget prioritization reveals as much about the challenges as the opportunities. The rush to invest in AI has notably outpaced strategic planning—comprehensive AI strategies for CRE remain absent in most organizations. 

While some companies proactively embrace the technology based on genuine conviction, a considerable portion of CRE teams implement AI not by choice, but by C-suite mandate viewing AI adoption as competitive necessity.

This strategic gap translates directly into execution challenges. While 92% are piloting AI, only 5% report having achieved most program goals. Though implementation is widespread, most initiatives remain experimental with limited scaling.

This raises a critical question: if achieving AI goals is challenging, how are we deciding where to focus limited resources?

Our research identified around 27 AI use cases across every aspect of the CRE value chain, from which occupiers are pursuing an average of five pilot projects simultaneously. 

Conventional wisdom suggests organizations should start with simple, low-risk applications—the 

so-called ‘low-hanging fruit’. Lease abstraction, for example, is widely considered an ideal GenAI application due to its document-heavy nature and limited embeddedness with other workflows.

Yet our research reveals a different approach emerging in CRE: despite facing limited resources and uncertain outcomes, CRE teams are prioritizing high-impact areas that directly address their most pressing business challenges. 

Three areas emerge as top priorities for AI pilot selection:

1. Real estate data-related workflows 

CRE teams work with complex datasets covering every aspect of building management, from energy consumption and employee satisfaction to payments, space utilization, indoor environmental data and more. However, such real estate data has historically been fragmented or inconsistent, impacting the depth and accuracy of portfolio-level insights. Occupiers are now looking at the groundbreaking capabilities of AI to tackle these challenges, exploring use cases for standardizing data and detecting anomalies, integrating different data sources, and automating data reports and presentations to enable a deeper, more holistic understanding of CRE operations. These initiatives may not generate immediate cost savings, but they create the data infrastructure necessary for all subsequent AI applications.

JLL's Azara, powered by JLL Falcon, exemplifies this transformation by consolidating fragmented data sources from multiple systems into a unified cloud data platform with preconfigured integrations. For a client in the automobile industry, Azara transformed over 20 years of low-quality operational data into a single source of truth, providing unprecedented transparency and multi-dimensional visibility that enabled strategic decision-making for the first time.

2. Portfolio optimization

Amid ongoing market challenges, CRE portfolios are challenged to be agile, fluid and liquid as a key component in reducing operational costs, making portfolio optimization the most important baseline expectation for business leaders over the next three years. Space planning and location strategy are shifting from a once-a-decade ordeal to a quarterly requirement – and for very large occupiers, a continuous assessment to right-size their footprint and manage costs. The breadth of data involved in these processes means that AI can bring significant efficiencies, and many CRE leaders are piloting its use in portfolio analysis, optimization strategy and capital planning. 

JLL's Azara demonstrates AI's power in portfolio optimization through real-time utilization monitoring, occupancy metrics and portfolio-wide trend analysis. The platform integrates facilities management data with market intelligence to deliver total cost of occupancy insights, enabling the continuous assessment and proactive five-year capital planning that today's dynamic market demands.

3. Energy management

93% of occupiers agree that sustainability, energy efficiency and decarbonization remain key drivers for technology adoption, with many increasingly turning to AI to accelerate progress. Energy management has been proven critical to both environmental compliance and cost-reduction measures. Current initiatives focus on use cases that can deliver long-term resilience for organizations, including AI for energy tracking and analytics, decarbonization roadmap planning and automated HVAC control. Unlike data workflows or portfolio optimization, energy management offers more immediate, measurable returns on AI investment. It is often considered as one of the most mature categories of AI use.

JLL's Hank exemplifies how AI delivers immediate, measurable value in energy management by applying machine learning to solve HVAC programming inconsistencies and energy performance inefficiencies. By creating digital twins of buildings and plugging into existing building automation systems, Hank automatically optimizes energy efficiency, air quality and maintenance costs while improving tenant comfort.

The promise of technological leapfrogging—where organizations skip intermediate steps to adopt cutting-edge solutions—has long captivated business leaders facing technology gaps. In theory, AI offers the ultimate leapfrogging opportunity: companies with outdated systems could bypass incremental upgrades and jump directly to AI-powered solutions. 

However, our research exposes a sobering reality. Rather than leveling the playing field, AI adoption is widening the gap between technology leaders and laggards, with companies that already run successful technology programs pulling further ahead in AI outcomes.

This divergence occurs amid resource constraints. 65% of organizations report experiencing CRE tech budget pressures over the past two years, forcing difficult prioritization decisions precisely when AI investment demands are high.

These budget pressures, compounded with operational challenges, have impacted decision-making. More than half of companies report longer tech procurement decision-making periods compared to pre-COVID timelines. This leads to a paradox where organizations need to move quickly on AI initiatives while internal processes have become more cautious. 

Two factors are driving this slower pace: persistent talent gaps that limit organizations' ability to evaluate and implement new technologies and increasingly stringent ROI expectations that require more extensive business case development before approval. 

Nonetheless, facing similar pressures, organizations with successful technology programs achieve considerably more with their AI efforts. These companies have the foundational capabilities—mature data infrastructure, established change management processes, experienced teams—that AI success requires.

Conversely, over 60% of companies must address fundamental technology issues, such as duplicated functionality or dormant systems, before fully leveraging AI capabilities. They face a double burden: catching up on the fundamentals while competing in AI innovation.

Companies that already have a successful CRE tech program display a much more systematic approach to integrating new tools. They define roadmaps with clear success metrics, change management and processes for stakeholder engagement – particularly securing sponsorship from at least one C-suite leader. 

This robust systematic approach is the key to implementing AI successfully. To establish this foundation, our research highlights four priorities for occupiers to act on: 

1. Ground expectations for AI with a multi-phase plan

The most effective AI programs balance quick wins that build confidence and momentum against longer-term foundational systems that require more effort and testing but ultimately drive greater business value. For example, an occupier might implement AI tools for optimizing energy consumption – which is straightforward to assess – alongside solutions to achieve more complex outcomes such as increasing portfolio agility amid market challenges. 

2. Invest in AI talent and resources, internally and externally

Despite facing similar budget pressures, leading companies are better resourced in terms of AI skills and capabilities - and the greater the priority on nurturing innovation, the greater the return. Currently, only 33% of the workforce feel adequately trained on AI. Regarding sourcing AI capabilities, 70% of occupiers use multiple sourcing strategies: internal GenAI training, custom tool development, hiring AI talent and external partnerships with tech companies and service firms.

3. Strengthen data and cybersecurity systems

AI innovation must be supported by robust digital infrastructure that protects data and corporate systems. Retiring or upgrading legacy tech systems in CRE is an imminent challenge for CRE leaders to undertake without disrupting business functions or losing data. Such legacy systems represent key barriers to AI adoption, with 81% of companies reporting at least three existing systems that aren’t generating the expected results and 88% allocating budget to upgrade legacy technologies.

4. Align AI rollouts with corporate decision-making cycles

Technology adoption requires multi-level stakeholder buy-in and change management. Our survey respondents highlighted that the best time to adopt or change a technology system would be during other major changes to the business, such as an IT system overhaul, leadership restructuring, response to new regulatory requirements and/or during capital planning cycles. CRE professionals who align AI rollouts with planned organizational changes are best placed to secure resources and engage the workforce. 

Some take comfort in seeing AI pilots fail, dismissing meaningful actions by claiming the technology isn't mature enough. But there's no going back—AI transformation will only deepen from here. 

Looking towards 2030 and beyond, the purpose of current pilots isn't just immediate ROI, but also providing critical learnings to inform a more encompassing, longer-term AI strategy for CRE.

Occupiers that wait idly for technologies to mature in the hope of a ‘second mover advantage’ risk competitive obsolescence as they miss the chance to experiment and understand how AI can deliver value for their unique operations. Rather, the true ‘second mover advantage’ lies in resisting AI hype while using the time to strategize, test carefully chosen AI use cases and nurture CRE teams’ capabilities. 

In the long run, AI’s most enduring value will belong to companies that build adaptive capacity for waves of change we can't fully predict yet. It's not just about being more efficient or growing faster – it's about developing the organizational DNA to continuously evolve as AI capabilities advance. 

The time to start is now.